--- name: yolo-export description: > Use when exporting or deploying Ultralytics YOLO models in Platform or code — the Platform Export tab and yolo export/model.export() for ONNX, TensorRT, CoreML, OpenVINO, LiteRT, NCNN, ExecuTorch, and NPUs (RKNN, QNN, Hailo, Ascend, IMX, Axelera, DeepX), FP16/INT8 quantization, benchmarking, and non-Python runtimes. For inference with .pt weights or Platform endpoints, see yolo-inference. --- # Export, quantization & deployment ## Fastest route: export in Platform Open a completed model's **Export** tab, select one of the 20 formats, configure its arguments, and click **Start Export**. Platform runs CPU exports directly and asks for a target GPU where the format requires one (notably TensorRT); download the artifact when the job completes. Match TensorRT's selected GPU family and software environment to the deployment target, just as with a local engine build. Use Platform when you do not want to install each exporter toolchain locally. Use the Python/CLI path below for custom calibration, repeatable automation, local hardware builds, or immediate parity validation. See [Platform model export](https://docs.ultralytics.com/platform/train/models/#export-model). ## Quickstart ```python from ultralytics import YOLO model = YOLO("runs/detect/train/weights/best.pt") path = model.export(format="onnx") # returns the exported file/dir path ``` ```bash yolo export model=best.pt format=onnx ``` Exports load straight back into `YOLO()` for predict/val — same API: ```python model = YOLO("best.onnx") # or best.engine, best_openvino_model/, ... ``` ## Choose format by target hardware | Target | `format=` | Why | | -------------------------------------------------------- | ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | NVIDIA GPU / Jetson | `engine` (TensorRT) | fastest on NVIDIA; **build on the deployment device** — engines are not portable across GPUs/TRT versions | | Intel CPU/iGPU/NPU | `openvino` | ~3× CPU speedup | | Apple iOS/macOS | `coreml` | Neural Engine | | Android | `litert` (renamed from `tflite`) or `ncnn` | NCNN strong on ARM | | Raspberry Pi | `ncnn` | best ARM CPU latency | | PyTorch Edge | `executorch` | | | Cross-platform / unsure | `onnx` | runs everywhere; start here, specialize when latency demands | | NPUs (Rockchip/Qualcomm/Hailo/Huawei/Sony/Axelera/DeepX) | `rknn` / `qnn` / `hailo` / `ascend` / `imx` / `axelera` / `deepx` | `name=` selects the exact chip for rknn/qnn/hailo/ascend | Full 20-format matrix with per-format supported args: `format-matrix.md` (this folder). ## Key arguments | Arg | Default | Notes | | ----------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `imgsz` | 640 | baked into most formats — match what you'll feed it | | `quantize` | None | **the** precision arg (replaces deprecated `half`/`int8`): `16`/`fp16` FP16 (~2× smaller/faster, free lunch on GPU/NPU); `8`/`int8`/`w8a8` INT8 (~4× smaller, costs ~1–3 mAP, needs calibration); also `w8a16`, `w8a32` | | `data` | None | calibration dataset for INT8 — a few hundred varied val images; defaults to the task's calibration set (coco128 etc.) if omitted | | `dynamic` | False | variable input size/batch (ONNX/TensorRT) — costs some speed | | `batch` | 1 | max batch baked into the export | | `simplify` | True | simplify ONNX graph | | `opset` | newest | pin lower if the consumer runtime complains | | `nms` | False | bake NMS into the graph — irrelevant for YOLO26 (NMS-free end-to-end); needed for YOLO11/v8 in non-Python runtimes | | `workspace` | None | TensorRT builder GiB — lower if the build OOMs | | `device` | None | `device=0` required for TensorRT; also speeds INT8 calibration | | `fraction` | 1.0 | fraction of calibration data used | ## Verify parity after export (always) ```bash yolo val model=best.pt data=data.yaml # baseline yolo val model=best.onnx data=data.yaml # FP16 within ~0.1–0.3 mAP; INT8 ~1–3 ``` Large gaps mean a broken export (imgsz mismatch, missing NMS handling, bad calibration data) — not "expected quantization noise". Also eyeball one prediction against the .pt. ## Benchmark all formats empirically ```bash yolo benchmark model=best.pt data=data.yaml imgsz=640 # add quantize=16 to test FP16 ``` Produces mAP + latency per exportable format **on this machine** — benchmark on the deployment hardware, not your dev box. ## Consuming exports outside Python - In raw runtimes (C++, mobile, JS) **you** own preprocessing (letterbox resize, BGR→RGB, /255) and output decoding. - Output layout differs: YOLO26 is end-to-end (final `[x1,y1,x2,y2,conf,cls]` rows); YOLO11/v8 raw heads emit `[4+nc, anchors]` needing decode + NMS — check the output tensor shape first. This is the main reason to prefer YOLO26 for edge deployment. - Class names travel in export metadata where supported; otherwise ship the `names` map alongside the model. - Serving: `ultralytics.utils.triton.TritonRemoteModel` for Triton; `examples/` in the ultralytics repo has ONNXRuntime C++/Rust/Python references. ## Troubleshooting | Symptom | Fix | | ----------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | | Export crashes on missing package | most backends auto-install on first export; rerun. TensorRT must match your CUDA — install per NVIDIA docs | | `Unsupported ONNX opset` downstream | export with lower `opset=`, or upgrade the runtime | | TensorRT build OOM/slow | lower `workspace`, `batch=1`, `dynamic=False` | | Export much less accurate | imgsz mismatch; INT8 calibration data too small/unrepresentative (fix `data=`, or fall back to `quantize=16`); wrong custom pre/post-processing | | Engine fails on another machine | TensorRT engines are device+version specific — rebuild on target | | CoreML export fails on Windows | export on macOS or Linux | | Deprecation warnings for `half`/`int8`/`tflite` | auto-forwarded (`half→quantize=16`, `int8→quantize=8`, `tflite→litert`) — switch to the new names | ## Related pages - `format-matrix.md` — all 20 formats, artifacts produced, and which args each format supports. Read when using any format beyond onnx/engine/openvino/coreml. If the installed version rejects an argument, trust the error text (it lists valid values) and `yolo cfg` over this file.